aws-samples/sagemaker-genai-hosting-examples
Apr 2026 – Apr 2026
1 Month active
Languages Used
JSONPython
Technical Skills
AWS SageMakerData EngineeringData ScienceMachine LearningPythondata cleaning
Supreet Kaur
PROFILE
Supreet Kaur
Worked on the aws-samples/sagemaker-genai-hosting-examples repository to deliver a unified SageMaker Multi-Adapter Endpoint, enabling deployment of multiple fine-tuned models with LoRA adapters through a single endpoint. This approach reduced operational overhead and improved scalability by consolidating endpoints and streamlining deployment workflows. Leveraging Python and AWS SageMaker, the developer implemented practical examples for multi-adapter deployment and LoRA integration, supporting efficient experimentation. Additionally, they enhanced repository and dataset management by removing duplicate files, ignoring .ipynb_checkpoints, and deleting checkpoint data, which improved maintainability and data governance. The work focused on data engineering, machine learning, and robust data handling practices.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
1
Lines of code
1,161,972
Activity Months1
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Work History
April 2026
4 Commits • 1 Features
Apr 1, 2026April 2026 (2026-04) monthly summary for aws-samples/sagemaker-genai-hosting-examples: Delivered a unified SageMaker Multi-Adapter Endpoint enabling deployment of multiple fine-tuned models with LoRA adapters via a single endpoint, reducing operational overhead and improving scalability. Implemented practical samples for multi-adapter deployment and LoRA integration under 06-examples/01-train-deploy-LoRA. Performed repository and dataset artifact cleanup to enhance maintainability and data governance by removing duplicates, ignoring .ipynb_checkpoints, and deleting checkpoint data. These changes streamline deployment workflows, lower costs, and improve data management for faster iteration and reliable experiments.
4 Commits • 1 Features
Apr 1, 2026April 2026 (2026-04) monthly summary for aws-samples/sagemaker-genai-hosting-examples: Delivered a unified SageMaker Multi-Adapter Endpoint enabling deployment of multiple fine-tuned models with LoRA adapters via a single endpoint, reducing operational overhead and improving scalability. Implemented practical samples for multi-adapter deployment and LoRA integration under 06-examples/01-train-deploy-LoRA. Performed repository and dataset artifact cleanup to enhance maintainability and data governance by removing duplicates, ignoring .ipynb_checkpoints, and deleting checkpoint data. These changes streamline deployment workflows, lower costs, and improve data management for faster iteration and reliable experiments.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance95.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
AWS SageMakerData EngineeringData ScienceMachine LearningPythondata cleaningdata managementdataset handlingrepository management
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